
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase    :   str						=			logging.get_logger(__name__)

lowercase    :   Optional[int]						=			{
    'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/config.json',
    # See all BART models at https://huggingface.co/models?filter=bart
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'bart'
	_A								=	['past_key_values']
	_A								=	{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







	def __init__(   self						:Optional[Any]	,  a						:Dict=5_0_2_6_5	,  a						:Optional[Any]=1_0_2_4	,  a						:List[str]=1_2	,  a						:Union[str, Any]=4_0_9_6	,  a						:str=1_6	,  a						:Tuple=1_2	,  a						:Tuple=4_0_9_6	,  a						:int=1_6	,  a						:Optional[Any]=0.0	,  a						:Tuple=0.0	,  a						:Any="gelu"	,  a						:Any=1_0_2_4	,  a						:str=0.1	,  a						:Any=0.0	,  a						:List[str]=0.0	,  a						:Tuple=0.02	,  a						:Tuple=0.0	,  a						:Dict=False	,  a						:int=True	,  a						:int=3	,  a						:List[str]=1	,  a						:Tuple=0	,  a						:List[Any]=2	,  a						:List[Any]=True	,  a						:Union[str, Any]=2	,  a						:str=2	,  **a						:str	,  )						->					Dict:
								__UpperCamelCase      :  List[str]       =   vocab_size
								__UpperCamelCase      :  Optional[int]       =   max_position_embeddings
								__UpperCamelCase      :  str       =   d_model
								__UpperCamelCase      :  Union[str, Any]       =   encoder_ffn_dim
								__UpperCamelCase      :  Tuple       =   encoder_layers
								__UpperCamelCase      :  Dict       =   encoder_attention_heads
								__UpperCamelCase      :  Optional[int]       =   decoder_ffn_dim
								__UpperCamelCase      :  Any       =   decoder_layers
								__UpperCamelCase      :  int       =   decoder_attention_heads
								__UpperCamelCase      :  List[str]       =   dropout
								__UpperCamelCase      :  int       =   attention_dropout
								__UpperCamelCase      :  Any       =   activation_dropout
								__UpperCamelCase      :  Union[str, Any]       =   activation_function
								__UpperCamelCase      :  Union[str, Any]       =   init_std
								__UpperCamelCase      :  List[str]       =   encoder_layerdrop
								__UpperCamelCase      :  Dict       =   decoder_layerdrop
								__UpperCamelCase      :  List[str]       =   classifier_dropout
								__UpperCamelCase      :  Optional[int]       =   use_cache
								__UpperCamelCase      :  Optional[int]       =   encoder_layers
								__UpperCamelCase      :  Tuple       =   scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    num_labels=a	,  pad_token_id=a	,  bos_token_id=a	,  eos_token_id=a	,  is_encoder_decoder=a	,  decoder_start_token_id=a	,  forced_eos_token_id=a	,  **a	,  )

								# ensure backward compatibility for BART CNN models
								if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"	,  a		):
															__UpperCamelCase      :  Tuple       =   self.bos_token_id
															warnings.warn(
															    f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
															    "The config can simply be saved and uploaded again to be fixed."		)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	@property
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
															__UpperCamelCase      :  Union[str, Any]       =   OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ]		)

															if self.use_past:
																						__UpperCamelCase      :  Union[str, Any]       =   {0: "batch"}
																						__UpperCamelCase      :  List[str]       =   {0: "batch", 1: "past_decoder_sequence + sequence"}
															else:
																						__UpperCamelCase      :  Union[str, Any]       =   {0: "batch", 1: "decoder_sequence"}
																						__UpperCamelCase      :  Union[str, Any]       =   {0: "batch", 1: "decoder_sequence"}

															if self.use_past:
																						self.fill_with_past_key_values_(a	,  direction="inputs"		)
								elif self.task == "causal-lm":
															# TODO: figure this case out.
															__UpperCamelCase      :  Dict       =   OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															    ]		)
															if self.use_past:
																						__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   self.num_layers
																						for i in range(a		):
																													__UpperCamelCase      :  List[str]       =   {0: "batch", 2: "past_sequence + sequence"}
																													__UpperCamelCase      :  Union[str, Any]       =   {0: "batch", 2: "past_sequence + sequence"}
								else:
															__UpperCamelCase      :  Optional[int]       =   OrderedDict(
															    [
															        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
															        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
															        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
															        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
															    ]		)

								return common_inputs







	@property
	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
															__UpperCamelCase      :  Dict       =   super().outputs
								else:
															__UpperCamelCase      :  List[str]       =   super(a	,  self		).outputs
															if self.use_past:
																						__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   self.num_layers
																						for i in range(a		):
																													__UpperCamelCase      :  Optional[int]       =   {0: "batch", 2: "past_sequence + sequence"}
																													__UpperCamelCase      :  Optional[int]       =   {0: "batch", 2: "past_sequence + sequence"}
								return common_outputs







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:PreTrainedTokenizer	,  a						:int = -1	,  a						:int = -1	,  a						:bool = False	,  a						:Optional[TensorType] = None	,  )						->					Mapping[str, Any]:
								__UpperCamelCase      :  Optional[int]       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a	,  a	,  a	,  a	,  a		)

								# Generate decoder inputs
								__UpperCamelCase      :  List[Any]       =   seq_length if not self.use_past else 1
								__UpperCamelCase      :  int       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a	,  a	,  a	,  a	,  a		)
								__UpperCamelCase      :  Tuple       =   {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
								__UpperCamelCase      :  Dict       =   dict(**a	,  **a		)

								if self.use_past:
															if not is_torch_available():
																						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																						import torch
															__UpperCamelCase  ,		__UpperCamelCase      :  Any       =   common_inputs["input_ids"].shape
															__UpperCamelCase      :  int       =   common_inputs["decoder_input_ids"].shape[1]
															__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   self.num_attention_heads
															__UpperCamelCase      :  Optional[Any]       =   (
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															__UpperCamelCase      :  Optional[Any]       =   decoder_seq_length + 3
															__UpperCamelCase      :  Dict       =   (
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															__UpperCamelCase      :  Tuple       =   torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(a	,  a		)]	,  dim=1		)

															__UpperCamelCase      :  str       =   []
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															__UpperCamelCase  ,		__UpperCamelCase      :  List[Any]       =   self.num_layers
															__UpperCamelCase      :  Dict       =   min(a	,  a		)
															__UpperCamelCase      :  str       =   max(a	,  a		) - min_num_layers
															__UpperCamelCase      :  Tuple       =   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

															for _ in range(a		):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(a		),
																						        torch.zeros(a		),
																						        torch.zeros(a		),
																						        torch.zeros(a		),
																						    )		)
															# TODO: test this.
															__UpperCamelCase      :  List[Any]       =   encoder_shape if remaining_side_name == "encoder" else decoder_shape
															for _ in range(a	,  a		):
																						common_inputs["past_key_values"].append((torch.zeros(a		), torch.zeros(a		))		)
								return common_inputs







	def   _lowerCamelCase							(   self						:Optional[int]	,  a						:PreTrainedTokenizer	,  a						:int = -1	,  a						:int = -1	,  a						:bool = False	,  a						:Optional[TensorType] = None	,  )						->					Mapping[str, Any]:
								__UpperCamelCase      :  List[Any]       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    a	,  a	,  a	,  a	,  a		)

								if self.use_past:
															if not is_torch_available():
																						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																						import torch
															__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   common_inputs["input_ids"].shape
															# Not using the same length for past_key_values
															__UpperCamelCase      :  int       =   seqlen + 2
															__UpperCamelCase  ,		__UpperCamelCase      :  List[str]       =   self.num_layers
															__UpperCamelCase  ,		__UpperCamelCase      :  Dict       =   self.num_attention_heads
															__UpperCamelCase      :  Any       =   (
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															__UpperCamelCase      :  Any       =   common_inputs["attention_mask"].dtype
															__UpperCamelCase      :  str       =   torch.cat(
															    [common_inputs["attention_mask"], torch.ones(a	,  a	,  dtype=a		)]	,  dim=1		)
															__UpperCamelCase      :  Optional[int]       =   [
															    (torch.zeros(a		), torch.zeros(a		)) for _ in range(a		)
															]
								return common_inputs







	def   _lowerCamelCase							(   self						:int	,  a						:PreTrainedTokenizer	,  a						:int = -1	,  a						:int = -1	,  a						:bool = False	,  a						:Optional[TensorType] = None	,  )						->					Mapping[str, Any]:
								# Copied from OnnxConfig.generate_dummy_inputs
								# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								__UpperCamelCase      :  List[str]       =   compute_effective_axis_dimension(
								    a	,  fixed_dimension=OnnxConfig.default_fixed_batch	,  num_token_to_add=0		)

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								__UpperCamelCase      :  List[str]       =   tokenizer.num_special_tokens_to_add(a		)
								__UpperCamelCase      :  Union[str, Any]       =   compute_effective_axis_dimension(
								    a	,  fixed_dimension=OnnxConfig.default_fixed_sequence	,  num_token_to_add=a		)

								# Generate dummy inputs according to compute batch and sequence
								__UpperCamelCase      :  int       =   [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
								__UpperCamelCase      :  str       =   dict(tokenizer(a	,  return_tensors=a		)		)
								return common_inputs







	def   _lowerCamelCase							(   self						:Union[str, Any]	,  a						:PreTrainedTokenizer	,  a						:int = -1	,  a						:int = -1	,  a						:bool = False	,  a						:Optional[TensorType] = None	,  )						->					Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
															__UpperCamelCase      :  Union[str, Any]       =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    a	,  batch_size=a	,  seq_length=a	,  is_pair=a	,  framework=a		)

								elif self.task == "causal-lm":
															__UpperCamelCase      :  Any       =   self._generate_dummy_inputs_for_causal_lm(
															    a	,  batch_size=a	,  seq_length=a	,  is_pair=a	,  framework=a		)
								else:
															__UpperCamelCase      :  str       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    a	,  batch_size=a	,  seq_length=a	,  is_pair=a	,  framework=a		)

								return common_inputs







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:List[str]	,  a						:int	,  a						:List[str]	,  a						:Optional[Any]		)						->					Optional[Any]:
								if self.task in ["default", "seq2seq-lm"]:
															__UpperCamelCase      :  Dict       =   super()._flatten_past_key_values_(a	,  a	,  a	,  a		)
								else:
															__UpperCamelCase      :  Optional[Any]       =   super(a	,  self		)._flatten_past_key_values_(
															    a	,  a	,  a	,  a		)

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase    :   List[str]						=			get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	ReformerTokenizer
	_A								=	ReformerTokenizerFast
	_A								=	True
	_A								=	False
	_A								=	True







	def   _lowerCamelCase							(   self						:Dict		)						->					Union[str, Any]:
								super().setUp()

								__UpperCamelCase      :  List[str]       =   ReformerTokenizer(a	,  keep_accents=a		)
								tokenizer.save_pretrained(self.tmpdirname		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "<s>"
								__UpperCamelCase      :  Dict       =   1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(a		)	,  a		)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(a		)	,  a		)







	def   _lowerCamelCase							(   self						:Dict		)						->					List[Any]:
								__UpperCamelCase      :  int       =   list(self.get_tokenizer().get_vocab().keys()		)

								self.assertEqual(vocab_keys[0]	,  "<unk>"		)
								self.assertEqual(vocab_keys[1]	,  "<s>"		)
								self.assertEqual(vocab_keys[-1]	,  "j"		)
								self.assertEqual(len(a		)	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					str:
								self.assertEqual(self.get_tokenizer().vocab_size	,  1_0_0_0		)







	def   _lowerCamelCase							(   self						:List[str]		)						->					List[str]:
								if not self.test_rust_tokenizer:
															return

								__UpperCamelCase      :  Union[str, Any]       =   self.get_tokenizer()
								__UpperCamelCase      :  List[str]       =   self.get_rust_tokenizer()

								__UpperCamelCase      :  Union[str, Any]       =   "I was born in 92000, and this is falsé."

								__UpperCamelCase      :  List[str]       =   tokenizer.tokenize(a		)
								__UpperCamelCase      :  int       =   rust_tokenizer.tokenize(a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  List[str]       =   tokenizer.encode(a	,  add_special_tokens=a		)
								__UpperCamelCase      :  Tuple       =   rust_tokenizer.encode(a	,  add_special_tokens=a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  Union[str, Any]       =   self.get_rust_tokenizer()
								__UpperCamelCase      :  int       =   tokenizer.encode(a		)
								__UpperCamelCase      :  List[Any]       =   rust_tokenizer.encode(a		)
								self.assertListEqual(a	,  a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:int=1_5		)						->					Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																						__UpperCamelCase      :  List[Any]       =   self.rust_tokenizer_class.from_pretrained(a	,  **a		)

																						# Simple input
																						__UpperCamelCase      :  Union[str, Any]       =   "This is a simple input"
																						__UpperCamelCase      :  Dict       =   ["This is a simple input 1", "This is a simple input 2"]
																						__UpperCamelCase      :  Tuple       =   ("This is a simple input", "This is a pair")
																						__UpperCamelCase      :  Optional[Any]       =   [
																						    ("This is a simple input 1", "This is a simple input 2"),
																						    ("This is a simple pair 1", "This is a simple pair 2"),
																						]

																						# Simple input tests
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Simple input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(a	,  tokenizer_r.encode_plus	,  a	,  max_length=a	,  padding="max_length"		)

																						# Pair input
																						self.assertRaises(
																						    a	,  tokenizer_r.batch_encode_plus	,  a	,  max_length=a	,  padding="max_length"	,  )







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					str:
								pass







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Optional[Any]:
								__UpperCamelCase      :  List[Any]       =   ReformerTokenizer(a	,  keep_accents=a		)

								__UpperCamelCase      :  Tuple       =   tokenizer.tokenize("This is a test"		)
								self.assertListEqual(a	,  ["▁This", "▁is", "▁a", "▁t", "est"]		)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(a		)	,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]	,  )

								__UpperCamelCase      :  str       =   tokenizer.tokenize("I was born in 92000, and this is falsé."		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "9",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "é",
								        ".",
								    ]	,  )
								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_tokens_to_ids(a		)
								self.assertListEqual(
								    a	,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]	,  )

								__UpperCamelCase      :  Union[str, Any]       =   tokenizer.convert_ids_to_tokens(a		)
								self.assertListEqual(
								    a	,  [
								        SPIECE_UNDERLINE + "I",
								        SPIECE_UNDERLINE + "was",
								        SPIECE_UNDERLINE + "b",
								        "or",
								        "n",
								        SPIECE_UNDERLINE + "in",
								        SPIECE_UNDERLINE + "",
								        "<unk>",
								        "2",
								        "0",
								        "0",
								        "0",
								        ",",
								        SPIECE_UNDERLINE + "and",
								        SPIECE_UNDERLINE + "this",
								        SPIECE_UNDERLINE + "is",
								        SPIECE_UNDERLINE + "f",
								        "al",
								        "s",
								        "<unk>",
								        ".",
								    ]	,  )







	@cached_property
	def   _lowerCamelCase							(   self						:Any		)						->					List[str]:
								return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)







	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict:
								__UpperCamelCase      :  int       =   "Hello World!"
								__UpperCamelCase      :  Union[str, Any]       =   [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					List[str]:
								__UpperCamelCase      :  Dict       =   (
								    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
								    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
								)
								__UpperCamelCase      :  List[Any]       =   [
								    1_0_8,
								    2_6_5,
								    2_4,
								    1_1_1,
								    4,
								    2_5_8,
								    1_5_6,
								    3_5,
								    2_8,
								    2_7_5,
								    3,
								    2_5_9,
								    2_9_7,
								    2_6_0,
								    8_4,
								    4,
								    3_5,
								    1_1_0,
								    4_4,
								    8,
								    2_5_9,
								    9_1,
								    2_6_8,
								    2_1,
								    1_1,
								    2_0_9,
								    2_7_4,
								    1_0_9,
								    2_6_6,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    3_1_5,
								    2_5_8,
								    2_7_8,
								    2_5_8,
								    2_7_7,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_8,
								    2_5_8,
								    3_1_9,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    0,
								    2_5_8,
								    2_8_7,
								    2_5_8,
								    3_1_5,
								    2_5_8,
								    2_8_9,
								    2_5_8,
								    2_7_8,
								    9_9,
								    2_6_9,
								    2_6_6,
								    2_6_2,
								    8,
								    2_5_9,
								    2_4_1,
								    4,
								    2_1_7,
								    2_3_0,
								    2_6_8,
								    2_6_6,
								    5_5,
								    1_6_8,
								    1_0_6,
								    7_5,
								    1_9_3,
								    2_6_6,
								    2_2_3,
								    2_7,
								    4_9,
								    2_6,
								    2_8_2,
								    2_5,
								    2_6_4,
								    2_9_9,
								    1_9,
								    2_6,
								    0,
								    2_5_8,
								    2_7_7,
								    1_1_7,
								    8_6,
								    9_3,
								    1_7_6,
								    1_8_3,
								    2_7_0,
								    1_1,
								    2_6_2,
								    4_2,
								    6_1,
								    2_6_5,
								]

								self.assertListEqual(a	,  self.big_tokenizer.encode(a		)		)







	@require_torch
	@slow
	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[Any]:
								import torch

								from transformers import ReformerConfig, ReformerModel

								# Build sequence
								__UpperCamelCase      :  Tuple       =   list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
								__UpperCamelCase      :  Union[str, Any]       =   " ".join(a		)
								__UpperCamelCase      :  Tuple       =   self.big_tokenizer.encode_plus(a	,  return_tensors="pt"		)
								__UpperCamelCase      :  Optional[int]       =   self.big_tokenizer.batch_encode_plus([sequence, sequence]	,  return_tensors="pt"		)

								__UpperCamelCase      :  Optional[Any]       =   ReformerConfig()
								# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
								__UpperCamelCase      :  Tuple       =   encoded_sequence["input_ids"].shape
								__UpperCamelCase      :  int       =   ReformerModel(a		)

								# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

								with torch.no_grad():
															model(**a		)
															model(**a		)







	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Any:
								# fmt: off
								__UpperCamelCase      :  Any       =   {"input_ids": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# This tokenizer does not know some characters like ")".
								# That is the reason why we use very simple texts here.
								# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
								__UpperCamelCase      :  List[str]       =   [
								    "This is a very simple sentence.",
								    "The quick brown fox jumps over the lazy dog.",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=a	,  model_name="google/reformer-crime-and-punishment"	,  revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"	,  padding=a	,  sequences=a	,  )